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Contemporaneous Statistics for Estimation in Stochastic Actor-Oriented Co-evolution Models

Author

Listed:
  • Viviana Amati

    (ETH Zurich)

  • Felix Schönenberger

    (ETH Zurich)

  • Tom A. B. Snijders

    (University of Groningen
    University of Oxford
    University of Oxford)

Abstract

Stochastic actor-oriented models (SAOMs) can be used to analyse dynamic network data, collected by observing a network and a behaviour in a panel design. The parameters of SAOMs are usually estimated by the method of moments (MoM) implemented by a stochastic approximation algorithm, where statistics defining the moment conditions correspond in a natural way to the parameters. Here, we propose to apply the generalized method of moments (GMoM), using more statistics than parameters. We concentrate on statistics depending jointly on the network and the behaviour, because of the importance of their interdependence, and propose to add contemporaneous statistics to the usual cross-lagged statistics. We describe the stochastic algorithm developed to approximate the GMoM solution. A small simulation study supports the greater statistical efficiency of the GMoM estimator compared to the MoM.

Suggested Citation

  • Viviana Amati & Felix Schönenberger & Tom A. B. Snijders, 2019. "Contemporaneous Statistics for Estimation in Stochastic Actor-Oriented Co-evolution Models," Psychometrika, Springer;The Psychometric Society, vol. 84(4), pages 1068-1096, December.
    • Handle: RePEc:spr:psycho:v:84:y:2019:i:4:d:10.1007_s11336-019-09676-3
    DOI: 10.1007/s11336-019-09676-3
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    References listed on IDEAS

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    3. Hu, Zhibin & Wu, Guangdong & Han, Yilong & Niu, Yanliang, 2023. "Unraveling the dynamic changes of high-speed rail network with urban development: Evidence from China," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

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